INFLUENCE OF THE EVENT RATE ON DISCRIMINATION ABILITIES OF BANKRUPTCY PREDICTION MODELS

Lili Zhang1 , Jennifer Priestley2 , and Xuelei Ni3
1Program in Analytics and Data Science, Kennesaw State University, Georgia, USA
2Analytics and Data Science Institute, Kennesaw State University, Georgia, USA
3Department of Statistics, Kennesaw State University, Georgia, USA

ABSTRACT

In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared based on Kolmogorov-Smirnov Statistic, accuracy,F1 score, Type I error, Type II error, and ROC curve on the hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most insensitive to the event rate, while Support Vector Machine is the most sensitive.

KEYWORDS

Bankruptcy Prediction, Public Records, Firmographics, Event Rate, Discrimination Ability

Orginal Source URL: http://aircconline.com/ijdms/V10N1/10118ijdms01.pdf
http://airccse.org/journal/ijdms/current2018.html

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